296 research outputs found

    Sliding intermittent control for BAM neural networks with delays

    Get PDF
    Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2013/615947 Open AccessThis paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results

    Stochastic neural network dynamics: synchronisation and control

    Get PDF
    Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain’s performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson’s disease. As a result, it is desirable to research under which conditions synchronisation arises in neural networks and the possibility of controlling its prevalence. Stochastic ensembles of FitzHugh-Nagumo elements are used to model neural networks for numerical simulations and bifurcation analysis. The FitzHugh-Nagumo model is employed because of its realistic representation of the flow of sodium and potassium ions in addition to its advantageous property of allowing phase plane dynamics to be observed. Network characteristics such as connectivity, configuration and size are explored to determine their influences on global synchronisation generation in their respective systems. Oscillations in the mean-field are used to detect the presence of synchronisation over a range of coupling strength values. To ensure simulation efficiency, coupling strengths between neurons that are identical and fixed with time are investigated initially. Such networks where the interaction strengths are fixed are referred to as homogeneously coupled. The capacity of controlling and altering behaviours produced by homogeneously coupled networks is assessed through the application of weak and strong delayed feedback independently with various time delays. To imitate learning, the coupling strengths later deviate from one another and evolve with time in networks that are referred to as heterogeneously coupled. The intensity of coupling strength fluctuations and the rate at which coupling strengths converge to a desired mean value are studied to determine their impact upon synchronisation performance. The stochastic delay differential equations governing the numerically simulated networks are then converted into a finite set of deterministic cumulant equations by virtue of the Gaussian approximation method. Cumulant equations for maximal and sub-maximal connectivity are used to generate two-parameter bifurcation diagrams on the noise intensity and coupling strength plane, which provides qualitative agreement with numerical simulations. Analysis of artificial brain networks, in respect to biological brain networks, are discussed in light of recent research in sleep theor

    SpiNNaker - A Spiking Neural Network Architecture

    Get PDF
    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    Astrocytic modulation of neuronal network oscillations

    Get PDF
    The synchronization of the neuron’s membrane potential results in the emergence of neuronal oscillations at multiple frequencies that serve distinct physiological functions (e.g. facilitation of synaptic plasticity) and correlate with different behavioural states (e.g. sleep, wakefulness, attention). It has been postulated that at least ten distinct mechanisms are required to cover the large frequency range of neuronal oscillations in the cortex, including variations in the concentration of extracellular neurotransmitters and ions, as well as changes in cellular excitability. However, the mechanism that gears the transition between different oscillatory frequencies is still unknown. Over the past decade, astrocytes have been the focus of much research, mainly due to (1) their close association with synapses forming what is known today as the “tripartite synapse”, which allows them to bidirectionally interact with neurons and modulate synaptic transmission; (2) their syncytium-like activity, as they are electrically coupled via gap junctions and actively communicate through Ca2+ waves; and (3) their ability to regulate neuronal excitability via glutamate uptake and tight control of the extracellular K+ levels via a process termed K+ clearance. In this thesis we hypothesized that astrocytes, in addition to their role as modulators of neuronal excitability, also act as “network managers” that can modulate the overall network oscillatory activity within their spatial domain. To do so, it is proposed that astrocytes fine-tune their K+ clearance capabilities to affect neuronal intrinsic excitability properties and synchronization with other neurons, thus mediating the transitions between neuronal network oscillations at different frequencies. To validate or reject this hypothesis I have investigated the potential role of astrocytes in modulating cortical oscillations at both cellular and network levels, aiming at answering three main research questions: a) what is the impact of alterations in astrocytic K+ clearance mechanisms on cortical networks oscillatory dynamics? b) what specific neuronal properties underlying the generation of neuronal oscillations are affected as a result of impairments in the astrocytic K+ clearance process? and c) what are the bidirectional mechanisms between neurons and astrocytes (i.e. neuromodulators) that specifically affect the K+ clearance process to modulate the network activity output? In the first experimental chapter I used electrophysiological recordings and pharmacological manipulations to dissect the contribution of the different astrocytic K+ clearance mechanisms to the modulation of neuronal network oscillations at multiple frequencies. A key finding was that alterations in membrane properties of layer V pyramidal neurons strongly correlated with the network behaviour following impairments in astrocytic K+ clearance capabilities, depicted as enhanced excitability underlying the amplification of high-frequency oscillations, especially within the beta and gamma range. The second experimental chapter describes a combinatorial approach based on K+-selective microelectrode recordings and optical imaging of K+ ions used to quantitatively determine extracellular K+ changes and to follow the spatiotemporal distribution of K+ ions under both physiological and altered K+ clearance conditions, which affected the K+ clearance rate. The impact of different neuromodulators on astrocytic function is discussed in the third experimental chapter. Using extracellular K+ recordings and Ca2+ imaging I found that some neuromodulators act specifically on astrocytic receptors to affect both K+ clearance mechanisms and Ca2+ signalling, as evidenced by reduced K+ clearance rates and altered evoked Ca2+ signals. Overall, this thesis provides new insights regarding the impact of astrocytic K+ clearance mechanisms on modulating neuronal properties at both cellular and network levels, which in turn imposes alterations on neuronal oscillations that are associated with different behavioural states

    SpiNNaker - A Spiking Neural Network Architecture

    Get PDF
    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    Principles of excitatory and inhibitory functional connectivity in cerebellar cortex in vivo

    Get PDF
    Determining the functional impact of single interneurons on neuronal output, and how interneurons are recruited by physiological patterns of excitation, are crucial to our understanding of inhibition. In the cerebellar cortex, molecular layer interneurons and their targets, Purkinje cells, receive excitatory inputs from granule cells and climbing fibres, the latter signalling to interneurons via glutamate spillover. How these feed-forward pathways are engaged by physiological patterns of activity in vivo is insufficiently understood. Using dual patch-clamp recordings from interneurons and Purkinje cells in mice in vivo, I have probed the spatiotemporal interactions between these circuit elements. I demonstrate that single spikes in single interneurons can potently inhibit the spiking of Purkinje cells. Granule cell input activates both interneurons and the Purkinje cells they inhibit, generating local feed-forward inhibition. Climbing fibre input activates interneurons via glutamate spillover, but only rarely activates interneurons that inhibit spiking of the same Purkinje cell receiving the climbing fibre input. Rather, by activating inhibition among interneurons, climbing fibre glutamate spillover results in delayed inhibition of interneurons controlling Purkinje cell spike output, forming a disinhibitory motif. Functional climbing fibre-interneuron inhibition, inhibition among interneurons, and interneuron-Purkinje cell inhibition are vertically organised in the molecular layer, providing an anatomical substrate for this microcircuit motif. During sensory processing, these motifs account for pathway-specific recruitment of interneurons, generating fast and delayed excitatory interneuron responses via the granule cell and climbing fibre pathway, respectively. Sensory stimulation recruits granule cell input into INs and PCs near-simultaneously, resulting in rapid feed-forward inhibition. Together, these findings quantify the functional impact of single interneurons on their targets in vivo, and reveal how granule cell and climbing fibre inputs differentially recruit inhibitory microcircuits to diversify cerebellar computations

    Roles of GSK-3beta and PYK2 signaling pathways in synaptic plasticity

    Get PDF
    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2010.Cataloged from PDF version of thesis.Includes bibliographical references.Activity-dependent modification of synapses, as in long term potentiation (LTP) or long term depression (LTD), is widely believed to be a crucial mechanism for learning and memory. Molecular perturbations in these processes may underlie certain neuropsychiatric conditions. This thesis examines the role of two signaling pathways, glycogen synthase kinase 3 beta (GSK- 3beta) and proline-rich tyrosine kinase 2 (PYK2), in LTD at rat hippocampal synapses. GSK-3beta, a serine/threonine kinase implicated in the pathophysiology of schizophrenia, mood disorders, and Alzheimer's disease, is known to play a critical role in LTD. Here we report that GSK-3beta phosphorylates the postsynaptic scaffold protein PSD-95, a major determinant of synaptic strength, at the Thr- 19 residue. In hippocampal neurons, this promotes the activity-dependent dispersal of synaptic PSD-95 clusters. We found that overexpression of a phospho-null mutant (Ti 9A-PSD-95), but not a phospho-mimic mutant, blocks LTD without affecting basal synaptic function relative to wild type PSD-95 overexpression. Thus PSD-95 phosphorylation by GSK-3beta is a necessary step in LTD. [This project is a collaboration with Myung Jong Kim, and I am second author of the manuscript.] PYK2 is a calcium-dependent tyrosine kinase that is activated in cerebral ischemia and seizures. PYK2 is also known to bind PSD-95 at a region implicated in LTD signaling. Here we report a novel role for PYK2 in LTD. Chemical LTD treatment induces PYK2 phosphorylation at Tyr-402, and small hairpin RNA-mediated knockdown of PYK2 blocks LTD, but not LTP. We identify both enzymatic and non-enzymatic (scaffolding) roles for PYK2 in LTD, and find that PYK2 is required to suppress activity-dependent phosphorylation of the mitogen activated protein kinase ERK. ERK activity is believed to promote glutamate receptor insertion at synapses. Overexpression of WT-PYK2 further depresses activity-dependent ERK phosphorylation, and inhibits LTP, but not LTD. Our studies support a model whereby PYK2 antagonizes ERK signaling to promote LTD, at the expense of LTP, in hippocampal neurons. [This project is a collaboration with Myung Jong Kim and Chi-Fong Wang, and I am first author of the manuscript.]by Honor Hsin.Ph.D

    The 1988 Goddard Conference on Space Applications of Artificial Intelligence

    Get PDF
    This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies
    • 

    corecore